Introduction: The aim of this study was to establish the value of thalium-(201) single-photon emission computed tomography ((201)Tl-SPECT) in the detection of recurrences in the follow-up of patients with treated primary neuroepithelial tumours.
Material And Methods: Sixty-three (201)Tl-SPECT were performed in 36 patients with glioma (12 males, mean age of 46 +/- 13 years). All patients underwent surgery and adjuvant radiotherapy (and some of them received chemotherapy). All patients were submitted to morphological neuroimaging techniques as well (and (201) Tl-SPECT). Mean follow-up was 18.3 +/- 14.6 months. Gold standard was based on clinical follow-up, therapeutical decisions (at least 4 months after (201)Tl-SPECT) and imaging features.
Results: Sensitivity and specificity of (201)Tl-SPECT to detect glioma recurrences were 90% and 100% respectively and 93% accuracy. Sensitivity and specificity for high grade tumours, were 100% respectively. Due to 4 false negatives, sensitivity and specificity for low grade gliomas were 78% and 100%. In the positive (201)Tl-SPECT group of patients overall survival was 13.64% at the end of the study. The negative (201)Tl-SPECT group had 84.62% overall survival at the end of the study (p = 0.0003). CONCLUSIONS. (201)Tl-SPECT is a valuable and noninvasive diagnostic procedure to detect recurrence or progression disease for treated gliomas and ependymomas. (201)Tl-SPECT has a good correlation with short term prognosis with excellent diagnostic accuracy.
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http://dx.doi.org/10.1007/s12094-006-0122-9 | DOI Listing |
JMIR Med Inform
January 2025
Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Background: Many tools have been developed to predict the risk of diabetes in a population without diabetes; however, these tools have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, and low sensitivity or specificity.
Objective: We aimed to develop and validate an easy, systematic index for predicting diabetes risk in the Asian population.
Methods: We collected the data from the NAGALA (NAfld [nonalcoholic fatty liver disease] in the Gifu Area, Longitudinal Analysis) database.
BMC Gastroenterol
January 2025
Department of Nephrology, QingPu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, People's Republic of China.
Background: Gallstone disease (GSD) is associated with obesity. The Cardiometabolic Index (CMI), a metric that accurately assesses central adiposity and visceral fat, has not been extensively studied in relation to GSD risk. This study investigates the link between CMI and GSD incidence in U.
View Article and Find Full Text PDFBMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
BMC Public Health
January 2025
Department of Statistics and Data Science, Jahangirnagar University, Dhaka, 1342, Bangladesh.
Background: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
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